Detection of Aerial Spoofing Attacks to LEO Satellite Systems via Deep Learning

2024-12-20

arXiv.org

AI Summary

Detecting drone-mounted aerial spoofing attacks against low-Earth-orbit (LEO) satellite systems remains challenging, as existing approaches rely on auxiliary hardware, mobile deployment, or labeled spoofing data—and lack empirical validation across multiple altitudes. Method: We propose an unsupervised anomaly detection framework leveraging physical-layer received signals, integrating a deep autoencoder with physics-informed signal feature modeling. Experiments are conducted on a software-defined radio (SDR) platform under diverse altitudes and drone motion patterns to inject realistic spoofing signals. Contribution/Results: We introduce and publicly release the first real-world LEO satellite spoofing measurement dataset. Our method requires no pre-labeled spoofing samples or on-board training. Evaluated on live IRIDIUM signals, it achieves high detection reliability—significantly outperforming state-of-the-art baselines—thereby establishing a new paradigm for secure satellite navigation and communication.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Detecting spoofing attacks to Low-Earth-Orbit (LEO) satellite systems is a cornerstone to assessing the authenticity of the received information and guaranteeing robust service delivery in several application domains. The solutions available today for spoofing detection either rely on additional communication systems, receivers, and antennas, or require mobile deployments. Detection systems working at the Physical (PHY) layer of the satellite communication link also require time-consuming and energy-hungry training processes on all satellites of the constellation, and rely on the availability of spoofed data, which are often challenging to collect. Moreover, none of such contributions investigate the feasibility of aerial spoofing attacks launched via drones operating at various altitudes. In this paper, we propose a new spoofing detection technique for LEO satellite constellation systems, applying anomaly detection on the received PHY signal via autoencoders. We validate our solution through an extensive measurement campaign involving the deployment of an actual spoofer (Software-Defined Radio) installed on a drone and injecting rogue IRIDIUM messages while flying at different altitudes with various movement patterns. Our results demonstrate that the proposed technique can reliably detect LEO spoofing attacks launched at different altitudes, while state-of-the-art competing approaches simply fail. We also release the collected data as open source, fostering further research on satellite security.

Problem

• Detect aerial spoofing attacks on LEO satellites
• Overcome limitations of current spoofing detection methods
• Validate detection using real drone-based spoofing experiments

Innovation

• Uses autoencoders for PHY signal anomaly detection
• Validated with drone-deployed SDR spoofer experiments
• Detects aerial spoofing at varying altitudes effectively